Get popularity/download statistics for a package (recent download counts). Useful for judging how widely used and battle-tested a dependency is. Ecosystems: npm, pypi, cargo.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | Exact package name as published in that registry, e.g. express for npm, requests for pypi, serde for cargo. | |
| ecosystem | Yes | Package registry to look in. One of: npm, pypi, cargo. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must carry the full behavioral burden, but it only states 'recent download counts' without specifying the time window, whether data is aggregated per day/month, or any error handling/rate limits. The description does not contradict annotations, but it lacks the depth needed for a tool with zero annotation support.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences that are front-loaded with the core purpose and directly followed by the supported ecosystems. No filler or redundant detail; every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simplicity (2 params, no output schema) and absence of annotations, the description provides the essential purpose and ecosystems but omits details about the return format, temporal granularity, or any limitations. While adequate for a basic read tool, it leaves the agent to infer behavioral specifics that would normally come from annotations.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% and the schema provides thorough descriptions (e.g., 'exact package name as published in that registry', enum with all options). The description adds only the adjective 'recent' to hint at a temporal aspect, which is a minor addition. It does not compensate for any gaps since the schema already covers the parameters well.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a clear verb-action ('Get popularity/download statistics') and resource ('a package'), specifies the ecosystems (npm, pypi, cargo), and includes an explicit use case ('judging how widely used and battle-tested a dependency is'). This distinguishes it from sibling tools like package_versions or package_info by focusing on download counts.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides a clear context for use (assessing popularity and battle-testing) but does not explicitly mention alternatives or when not to use this tool. No sibling names are referenced, so the agent receives implied guidance only, not a definitive routing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool targets a distinct aspect of package intelligence: dependencies, download statistics, health, metadata, search, and version history. There is no overlap in purpose, and the descriptions clearly differentiate what each tool returns.
All tools follow the consistent pattern 'package_' plus a descriptive noun (dependencies, downloads, health, info, search, versions). The naming is uniform, snake_case, and immediately conveys each tool's function.
Six tools is well within the ideal 3–15 range and matches the server's single focus on package evaluation. Each tool covers a necessary facet without redundancy or bloat.
The tool surface covers the full lifecycle of package evaluation: discovering packages via search, inspecting metadata, checking version history, assessing popularity, and performing a composite health check including security advisories. No obvious gaps exist for the stated purpose.